Dynamic Relational Data Modeling for Scalable External Data Integration
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Solution Overview
Problem
Traditional graph-based databases are inefficient and ineffective in data retrieval and visualization due to their rigid semantic structure, which limits relational awareness and scalability, especially when integrating external data objects.
Innovation Solution
The introduction of dynamic data models that process relationships as absorbed associations based on attributes, using relational awareness scores and absorption scores to record and model relationships, enabling more significant relationships to be distinguished and integrated effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional graph-based databases are used to store data relationships, then data structure stability is maintained, but data retrieval efficiency and relational awareness deteriorate
Solution Approach 1:
The patent transforms static graph-based data relationships into dynamic relationships using neural network embeddings. Data objects are represented as vectors that can be dynamically adjusted through machine learning, allowing the system to adapt relationships based on absorption scores and contextual relevance rather than relying on fixed semantic structures.
Solution Approach 2:
The patent replaces traditional mechanical graph database structures with a machine learning-based system. Instead of using fixed nodes and edges in a graph, the system uses neural network embeddings, absorption score calculations, and vector-space relationships to represent and query data connections, enabling more efficient and flexible data retrieval.
2Quantity of substance
If external data objects are integrated into the database, then data completeness improves, but system scalability and integration efficiency deteriorate
Solution Approach 1:
The patent uses absorption scores as dynamic parameters to control data integration. Instead of integrating all external data objects uniformly, the system calculates absorption scores that quantify how well external objects match existing data patterns. This parameter-based approach allows efficient filtering and selective integration, maintaining productivity while improving data completeness.
Solution Approach 2:
The patent introduces neural network embeddings as an intermediary layer between external data objects and the core database system. This embedding layer transforms diverse external data into a unified vector representation space, enabling efficient comparison, matching, and integration without directly modifying the core database structure, thus maintaining scalability.
3Measurement precision
If relational awareness scores are calculated for all data relationships, then relationship discrimination accuracy improves, but computational load and processing time deteriorate
Solution Approach 1:
The patent applies partial action by calculating relational awareness scores selectively rather than for all possible relationships. The system uses absorption scores to identify the most relevant relationships and focuses computational resources on calculating awareness scores for those high-priority connections, achieving accurate relationship discrimination without the excessive computational burden of universal calculation.
Solution Approach 2:
The patent performs preliminary calculations of absorption scores and neural network embeddings before computing relational awareness scores. By pre-processing data objects and establishing their vector representations in advance, the system reduces the computational complexity of subsequent relationship analysis, enabling accurate discrimination with lower real-time computational load.
Data Source
AI summary
There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.